{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T02:09:43Z","timestamp":1722564583562},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3442381.3450045","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:37:45Z","timestamp":1622749065000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Graph Topic Neural Network for Document Representation"],"prefix":"10.1145","author":[{"given":"Qianqian","family":"Xie","sequence":"first","affiliation":[{"name":"Wuhan University, China"}]},{"given":"Jimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan University, China"}]},{"given":"Pan","family":"Du","sequence":"additional","affiliation":[{"name":"University of Montreal, Canada"}]},{"given":"Min","family":"Peng","sequence":"additional","affiliation":[{"name":"Wuhan University, China"}]},{"given":"Jian-Yun","family":"Nie","sequence":"additional","affiliation":[{"name":"University of Montreal, Canada"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Sami Abu-El-Haija Amol Kapoor Bryan Perozzi and Joonseok Lee. 2019. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In UAI. Sami Abu-El-Haija Amol Kapoor Bryan Perozzi and Joonseok Lee. 2019. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In UAI."},{"key":"e_1_3_2_1_2_1","unstructured":"James Atwood and Donald\u00a0F. Towsley. 2016. Diffusion-Convolutional Neural Networks. In NIPS. James Atwood and Donald\u00a0F. Towsley. 2016. Diffusion-Convolutional Neural Networks. In NIPS."},{"key":"e_1_3_2_1_3_1","volume-title":"Neural Relational Topic Models for Scientific Article Analysis. CIKM","author":"Bai Haoli","year":"2018","unstructured":"Haoli Bai , Zhuangbin Chen , Michael\u00a0 R. Lyu , Irwin King , and Zenglin Xu. 2018. Neural Relational Topic Models for Scientific Article Analysis. CIKM ( 2018 ). Haoli Bai, Zhuangbin Chen, Michael\u00a0R. Lyu, Irwin King, and Zenglin Xu. 2018. Neural Relational Topic Models for Scientific Article Analysis. CIKM (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.036103"},{"key":"e_1_3_2_1_5_1","volume-title":"Relational inductive biases, deep learning, and graph networks. arXiv","author":"Battaglia W","year":"2018","unstructured":"Peter\u00a0 W Battaglia , Jessica\u00a0 B Hamrick , Victor Bapst , Alvaro Sanchez-Gonzalez , Vinicius Zambaldi , Mateusz Malinowski , Andrea Tacchetti , David Raposo , Adam Santoro , Ryan Faulkner , 2018. Relational inductive biases, deep learning, and graph networks. arXiv ( 2018 ). Peter\u00a0W Battaglia, Jessica\u00a0B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, 2018. Relational inductive biases, deep learning, and graph networks. arXiv (2018)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Eliav Buchnik and Edith Cohen. 2019. Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity. In PERV. Eliav Buchnik and Edith Cohen. 2019. Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity. In PERV.","DOI":"10.1145\/3219617.3219621"},{"key":"e_1_3_2_1_8_1","unstructured":"Chen Cai and Yusu Wang. 2018. A simple yet effective baseline for non-attribute graph classification. ArXiv abs\/1811.03508(2018). Chen Cai and Yusu Wang. 2018. A simple yet effective baseline for non-attribute graph classification. ArXiv abs\/1811.03508(2018)."},{"key":"e_1_3_2_1_9_1","unstructured":"Ziqiang Cao Sujian Li Yang Liu Wenjie Li and Heng Ji. 2015. A novel neural topic model and its supervised extension. In AAAI. Ziqiang Cao Sujian Li Yang Liu Wenjie Li and Heng Ji. 2015. A novel neural topic model and its supervised extension. In AAAI."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Jonathan Chang and David\u00a0M. Blei. 2009. Relational Topic Models for Document Networks. In AISTATS. Jonathan Chang and David\u00a0M. Blei. 2009. Relational Topic Models for Document Networks. In AISTATS.","DOI":"10.1214\/09-AOAS309"},{"key":"e_1_3_2_1_11_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR. Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098017"},{"key":"e_1_3_2_1_13_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844\u20133852. Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844\u20133852."},{"key":"e_1_3_2_1_14_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1024\u20131034. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1024\u20131034."},{"key":"e_1_3_2_1_15_1","volume-title":"Revisiting graph neural networks: All we have is low-pass filters. arXiv","author":"Hoang NT","year":"2019","unstructured":"NT Hoang and Takanori Maehara . 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv ( 2019 ). NT Hoang and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Andreas Hotho Andreas N\u00fcrnberger and Gerhard Paa\u00df. 2005. A brief survey of text mining.. In Ldv Forum Vol.\u00a020. Citeseer 19\u201362. Andreas Hotho Andreas N\u00fcrnberger and Gerhard Paa\u00df. 2005. A brief survey of text mining.. In Ldv Forum Vol.\u00a020. Citeseer 19\u201362.","DOI":"10.21248\/jlcl.20.2005.68"},{"key":"e_1_3_2_1_17_1","first-page":"3435","article-title":"Text Level Graph Neural Network for Text Classification","volume":"2019","author":"Huang Lianzhe","year":"2019","unstructured":"Lianzhe Huang , Dehong Ma , Sujian Li , Xiaodong Zhang , and WANG Houfeng . 2019 . Text Level Graph Neural Network for Text Classification . In EMNLP-IJCNLP 2019. 3435 \u2013 3441 . Lianzhe Huang, Dehong Ma, Sujian Li, Xiaodong Zhang, and WANG Houfeng. 2019. Text Level Graph Neural Network for Text Classification. In EMNLP-IJCNLP 2019. 3435\u20133441.","journal-title":"EMNLP-IJCNLP"},{"key":"e_1_3_2_1_18_1","volume-title":"Bag of tricks for efficient text classification. arXiv","author":"Joulin Armand","year":"2016","unstructured":"Armand Joulin , Edouard Grave , Piotr Bojanowski , and Tomas Mikolov . 2016. Bag of tricks for efficient text classification. arXiv ( 2016 ). Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv (2016)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746\u20131751. Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746\u20131751.","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_1_20_1","volume-title":"Kingma and Max Welling","author":"P.","year":"2014","unstructured":"Diederik\u00a0 P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. CoRR abs\/1312.6114(2014). Diederik\u00a0P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. CoRR abs\/1312.6114(2014)."},{"key":"e_1_3_2_1_21_1","unstructured":"Thomas Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. ArXiv abs\/1611.07308(2016). Thomas Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. ArXiv abs\/1611.07308(2016)."},{"key":"e_1_3_2_1_22_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR. Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_1_23_1","volume-title":"Le and Hady\u00a0Wirawan Lauw","author":"Tuan","year":"2014","unstructured":"Tuan M.\u00a0V. Le and Hady\u00a0Wirawan Lauw . 2014 . Probabilistic Latent Document Network Embedding. 2014 ICDM ( 2014), 270\u2013279. Tuan M.\u00a0V. Le and Hady\u00a0Wirawan Lauw. 2014. Probabilistic Latent Document Network Embedding. 2014 ICDM (2014), 270\u2013279."},{"key":"e_1_3_2_1_24_1","volume-title":"TMSA: A Mutual Learning Model for Topic Discovery and Word Embedding","author":"Li Dingcheng","year":"2019","unstructured":"Dingcheng Li , Jingyuan Zhang , and Ping Li . 2019 . TMSA: A Mutual Learning Model for Topic Discovery and Word Embedding . In SIAM. SIAM , 684\u2013692. Dingcheng Li, Jingyuan Zhang, and Ping Li. 2019. TMSA: A Mutual Learning Model for Topic Discovery and Word Embedding. In SIAM. SIAM, 684\u2013692."},{"key":"e_1_3_2_1_25_1","volume-title":"Label Efficient Semi-Supervised Learning via Graph Filtering. 2019 CVPR","author":"Li Qimai","year":"2019","unstructured":"Qimai Li , Xiao-Ming Wu , Hongmei Liu , Xiaotong Zhang , and Zhichao Guan . 2019. Label Efficient Semi-Supervised Learning via Graph Filtering. 2019 CVPR ( 2019 ), 9574\u20139583. Qimai Li, Xiao-Ming Wu, Hongmei Liu, Xiaotong Zhang, and Zhichao Guan. 2019. Label Efficient Semi-Supervised Learning via Graph Filtering. 2019 CVPR (2019), 9574\u20139583."},{"key":"e_1_3_2_1_26_1","unstructured":"Renjie Liao Zhizhen Zhao Raquel Urtasun and Richard\u00a0S. Zemel. 2019. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ArXiv abs\/1901.01484(2019). Renjie Liao Zhizhen Zhao Raquel Urtasun and Richard\u00a0S. Zemel. 2019. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ArXiv abs\/1901.01484(2019)."},{"key":"e_1_3_2_1_27_1","volume-title":"Graph Convolutional Topic Model for Data Streams. arXiv","author":"Linh Ngo\u00a0Van","year":"2020","unstructured":"Ngo\u00a0Van Linh , Tran\u00a0Xuan Bach , and Khoat Than . 2020. Graph Convolutional Topic Model for Data Streams. arXiv ( 2020 ), arXiv\u20132003. Ngo\u00a0Van Linh, Tran\u00a0Xuan Bach, and Khoat Than. 2020. Graph Convolutional Topic Model for Data Streams. arXiv (2020), arXiv\u20132003."},{"key":"e_1_3_2_1_28_1","volume-title":"Recurrent neural network for text classification with multi-task learning","author":"Liu Pengfei","unstructured":"Pengfei Liu , Xipeng Qiu , and Xuanjing Huang . 2016. Recurrent neural network for text classification with multi-task learning . In IJCAI. AAAI Press , 2873\u20132879. Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. In IJCAI. AAAI Press, 2873\u20132879."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Qingqing Long Yilun Jin Guojie Song Yi Li and Wei Lin. 2020. Graph Structural-topic Neural Network. In SIGKDD. 1065\u20131073. Qingqing Long Yilun Jin Guojie Song Yi Li and Wei Lin. 2020. Graph Structural-topic Neural Network. In SIGKDD. 1065\u20131073.","DOI":"10.1145\/3394486.3403150"},{"key":"e_1_3_2_1_30_1","unstructured":"Alireza Makhzani and Brendan\u00a0J. Frey. 2014. k-Sparse Autoencoders. CoRR abs\/1312.5663(2014). Alireza Makhzani and Brendan\u00a0J. Frey. 2014. k-Sparse Autoencoders. CoRR abs\/1312.5663(2014)."},{"key":"e_1_3_2_1_31_1","volume-title":"Conference on Artificial Intelligence and Natural Language. Springer, 167\u2013180","author":"Potapenko Anna","year":"2017","unstructured":"Anna Potapenko , Artem Popov , and Konstantin Vorontsov . 2017 . Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks . In Conference on Artificial Intelligence and Natural Language. Springer, 167\u2013180 . Anna Potapenko, Artem Popov, and Konstantin Vorontsov. 2017. Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks. In Conference on Artificial Intelligence and Natural Language. Springer, 167\u2013180."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Salah Rifai Pascal Vincent Xavier Muller Xavier Glorot and Yoshua Bengio. 2011. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction. In ICML. Salah Rifai Pascal Vincent Xavier Muller Xavier Glorot and Yoshua Bengio. 2011. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction. In ICML.","DOI":"10.1007\/978-3-642-23783-6_41"},{"key":"e_1_3_2_1_33_1","volume-title":"Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. arXiv","author":"Shen Dinghan","year":"2018","unstructured":"Dinghan Shen , Guoyin Wang , Wenlin Wang , Martin\u00a0Renqiang Min , Qinliang Su , Yizhe Zhang , Chunyuan Li , Ricardo Henao , and Lawrence Carin . 2018. Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. arXiv ( 2018 ). Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin\u00a0Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, and Lawrence Carin. 2018. Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms. arXiv (2018)."},{"key":"e_1_3_2_1_34_1","unstructured":"Akash Srivastava and Charles Sutton. 2017. Autoencoding Variational Inference For Topic Models. In ICLR. Akash Srivastava and Charles Sutton. 2017. Autoencoding Variational Inference For Topic Models. In ICLR."},{"key":"e_1_3_2_1_35_1","unstructured":"Kai\u00a0Sheng Tai Richard Socher and Christopher\u00a0D Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075(2015). Kai\u00a0Sheng Tai Richard Socher and Christopher\u00a0D Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075(2015)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218473"},{"key":"e_1_3_2_1_37_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR. Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_38_1","unstructured":"Petar Velickovic William Fedus William\u00a0L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R.\u00a0Devon Hjelm. 2019. Deep Graph Infomax. ArXiv abs\/1809.10341(2019). Petar Velickovic William Fedus William\u00a0L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R.\u00a0Devon Hjelm. 2019. Deep Graph Infomax. ArXiv abs\/1809.10341(2019)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1953039"},{"key":"e_1_3_2_1_40_1","volume-title":"Joint embedding of words and labels for text classification. arXiv","author":"Wang Guoyin","year":"2018","unstructured":"Guoyin Wang , Chunyuan Li , Wenlin Wang , Yizhe Zhang , Dinghan Shen , Xinyuan Zhang , Ricardo Henao , and Lawrence Carin . 2018. Joint embedding of words and labels for text classification. arXiv ( 2018 ). Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, and Lawrence Carin. 2018. Joint embedding of words and labels for text classification. arXiv (2018)."},{"key":"e_1_3_2_1_41_1","unstructured":"Zhengjue Wang Chaojie Wang Hao Zhang Zhibin Duan Mingyuan Zhou and Bo Chen. 2020. Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification. AISTATS. Zhengjue Wang Chaojie Wang Hao Zhang Zhibin Duan Mingyuan Zhou and Bo Chen. 2020. Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classification. AISTATS."},{"key":"e_1_3_2_1_42_1","volume-title":"Simplifying Graph Convolutional Networks. In ICML","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri H.\u00a0 Souza Jr ., Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian\u00a0 Q. Weinberger . 2019 . Simplifying Graph Convolutional Networks. In ICML 2019. 6861\u20136871. http:\/\/proceedings.mlr.press\/v97\/wu19e.html Felix Wu, Amauri H.\u00a0Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian\u00a0Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML 2019. 6861\u20136871. http:\/\/proceedings.mlr.press\/v97\/wu19e.html"},{"key":"e_1_3_2_1_43_1","volume-title":"International conference on machine learning. PMLR, 6861\u20136871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 . Simplifying graph convolutional networks . In International conference on machine learning. PMLR, 6861\u20136871 . Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861\u20136871."},{"key":"e_1_3_2_1_44_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks. arXiv:1810.00826. Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks. arXiv:1810.00826."},{"key":"e_1_3_2_1_45_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?ArXiv abs\/1810.00826(2019). Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?ArXiv abs\/1810.00826(2019)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Liang Yang Fan Wu Junhua Gu Chuan Wang Xiaochun Cao Di Jin and Yuanfang Guo. 2020. Graph Attention Topic Modeling Network. In WWW. 144\u2013154. Liang Yang Fan Wu Junhua Gu Chuan Wang Xiaochun Cao Di Jin and Yuanfang Guo. 2020. Graph Attention Topic Modeling Network. In WWW. 144\u2013154.","DOI":"10.1145\/3366423.3380102"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Liang Yao Chengsheng Mao and Yuan Luo. 2019. Graph convolutional networks for text classification. In AAAI. Liang Yao Chengsheng Mao and Yuan Luo. 2019. Graph convolutional networks for text classification. In AAAI.","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Ce Zhang and Hady\u00a0W. Lauw. 2020. Topic Modeling on Document Networks with Adjacent-Encoder. In AAAI. Ce Zhang and Hady\u00a0W. Lauw. 2020. Topic Modeling on Document Networks with Adjacent-Encoder. In AAAI.","DOI":"10.1609\/aaai.v34i04.6152"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Qile Zhu Zheng Feng and Xiaolin Li. 2018. GraphBTM: Graph enhanced autoencoded variational inference for biterm topic model. In EMNLP. 4663\u20134672. Qile Zhu Zheng Feng and Xiaolin Li. 2018. GraphBTM: Graph enhanced autoencoded variational inference for biterm topic model. In EMNLP. 4663\u20134672.","DOI":"10.18653\/v1\/D18-1495"}],"event":{"name":"WWW '21: The Web Conference 2021","location":"Ljubljana Slovenia","acronym":"WWW '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3450045","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T05:33:03Z","timestamp":1673415183000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3450045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":49,"alternative-id":["10.1145\/3442381.3450045","10.1145\/3442381"],"URL":"http:\/\/dx.doi.org\/10.1145\/3442381.3450045","relation":{},"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"2021-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}